Particle Methods as Message Passing

Abstract

It is shown how particle methods can be viewed as message passing on factor graphs. In this setting, particle methods can readily be combined with other message-passing techniques such as the sum-product and max-product algorithm, expectation maximization, iterative conditional modes, steepest descent, Kaiman filters, etc. Generic message computation rules for particle-based representations of sum-product messages are formulated. Various existing particle methods are described as instances of those generic rules, i.e., Gibbs sampling, importance sampling, Markov-chain Monte Carlo methods (MCMC), particle filtering, and simulated annealing